Cloud Basics for Data Professionals

Overview

Learn basic cloud technology for data science & analytics, and get to know key players like AWS, GCP, Azure & Snowflake What you'll learn:

Learn why so many companies are leveraging cloud technology for data science & analyticsUnderstand the basic use cases of cloud computing for different data roles (analysts, data scientists & data engineers)Compare the different types of cloud services (IaaS, PaaS, and SaaS) and cloud infrastructure (private, public & hybrid)Explore the similarities and differences between major players in the cloud landscape, including Amazon Web Services, Microsoft Azure & Google CloudDemo real-world data analytics workflows using Azure, GCP, AWS and Snowflake This course is a high-level introduction to the world of cloud computing.The cloud ecosystem has grown exponentially in recent years, now storing more than half of the world’s corporate data. Yet most people who interact with cloud services are unaware of what’s going on behind the scenes.In this course, we’ll set the stage by defining what cloud computing means and why companies rely on it, draw comparisons against traditional on-premise computing, and explore how different types of data professionals interact with cloud technology.From there we’ll dig into the core components of cloud architecture, compare different types of cloud services and infrastructure, and review important topics like security, virtualization, cost control, and more.Next, we’ll explore the modern cloud landscape, and compare services offered by key players like AWS, Microsoft Azure, and Google Cloud.

We’ll introduce public and private cloud providers, data platforms and software products, and discuss how to mitigate the risk of vendor lock-in.Last but not least, we’ll walk through unique demos and real-world use cases to showcase how you can begin to leverage these services as a data professional, including workflows built on AWS, Azure, GCP and Snowflake.COURSEOUTLINE:

Cloud 101Introduce the basics of cloud computing, including what it is, why companies use it, and the way different data roles interact with itCloud ArchitectureUnderstand the core components of cloud computing and cloud infrastructure, as well as the types of cloud services and architectureThe Cloud LandscapeReview some of the major players in the cloud computing industry for data analytics, and compare their similarities and differencesCloud Data StacksDemo simple data analytics pipelines using combinations of cloud products and services (or “stacks”), including AWS, MySQLWorkbench, GCP, Looker, Azure, Snowflake, and more__________Ready to dive in? Join todayand get immediate, LIFETIME accessto the following:

2 hours of high-quality video4 real-world cloud demos & case studies3 course quizzesCloud Basics for Data Professionals ebook (50+pages)Expert support and Q&Aforum30-day Udemy satisfaction guaranteeWhether you’re an analyst or data scientist interested in cloud computing or a business leader looking to learn about the cloud landscape, this course is for you.Happy learning!-Chris Bruehl (Data Science Expert & Lead Python Instructor, Maven Analytics)__________Looking for our full business intelligence stack?

Search for "Maven Analytics"to browse our full course library, including Excel, Power BI, MySQL,Tableauand Machine Learning courses!See why our courses are among the TOP-RATEDon Udemy:

"Some of the BESTcourses I've ever taken. I've studied several programming languages, Excel, VBA and web dev, and Maven is among the very best I've seen!" Russ C."This is my fourth course from Maven Analytics and my fourth 5-star review, so I'm running out of things to say.

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Syllabus

• Introduction to Cloud Computing
Definition and Key Concepts
Benefits and Challenges of Cloud Computing
• Cloud Service Models
Infrastructure as a Service (IaaS)
Platform as a Service (PaaS)
Software as a Service (SaaS)
• Cloud Deployment Models
Public Cloud
Private Cloud
Hybrid Cloud
Community Cloud
• Major Cloud Service Providers
Amazon Web Services (AWS)
Microsoft Azure
Google Cloud Platform (GCP)
• Core Cloud Technologies for Data Professionals
Storage Solutions
Object Storage
Block Storage
File Storage
Compute Options
Virtual Machines
Containers and Kubernetes
Networking in the Cloud
• Security and Compliance in Cloud Computing
Data Privacy and Protection
Compliance Standards
• Cloud Cost Management
Pricing Models
Best Practices for Optimization
• Case Studies and Real-World Applications
Migrating to the Cloud
Examples of Cloud Use in Data Projects
• Summary and Future Trends in Cloud Computing
Emerging Technologies
The Role of AI and Machine Learning in Cloud Platforms

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Taught by

Maven Analytics and Chris Bruehl

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Subjects

Programming